2025年7月1日发版
This commit is contained in:
workpc
parent
c85d11866f
commit
68ec1c8ffb
340
config_jingbo.py
340
config_jingbo.py
@ -93,163 +93,24 @@ data = {
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ClassifyId = 1214
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# # 变量定义--线上环境
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# server_host = '10.200.32.39'
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# login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
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# upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
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# upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
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# query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
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# # 上传数据项值
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# push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
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# # 上传停更数据到市场信息平台
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# push_waring_data_value_list_url = f"http://{server_host}/jingbo-api/api/basicBuiness/crudeOilWarning/crudeSaveOrupdate"
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# # 获取预警数据中取消订阅指标ID
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# get_waring_data_value_list_url = f"http://{server_host}/jingbo-api/api/basicBuiness/crudeOilWarning/dataList"
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# login_data = {
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# "data": {
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# "account": "api_dev",
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# "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
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# "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
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# "terminal": "API"
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# },
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# "funcModule": "API",
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# "funcOperation": "获取token"
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# }
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# upload_data = {
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# "funcModule": '研究报告信息',
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# "funcOperation": '上传原油价格预测报告',
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# "data": {
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# "groupNo": '', # 用户组id
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# "ownerAccount": '27663', # 报告所属用户账号 27663 - 刘小朋
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# "reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
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# "fileName": '', # 文件名称
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# "fileBase64": '', # 文件内容base64
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# "categoryNo": 'yyjgycbg', # 研究报告分类编码
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# "smartBusinessClassCode": 'YCJGYCBG', # 分析报告分类编码
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# "reportEmployeeCode": "E40482", # 报告人 E40482 - 管理员 0000027663 - 刘小朋
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# "reportDeptCode": "002000621000", # 报告部门 - 002000621000 SH期货研究部
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# "productGroupCode": "RAW_MATERIAL" # 商品分类
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# }
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# }
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# warning_data = {
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# "groupNo": '', # 用户组id
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# "funcModule": '原油特征停更预警',
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# "funcOperation": '原油特征停更预警',
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# "data": {
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# 'WARNING_TYPE_NAME': '特征数据停更预警',
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# 'WARNING_CONTENT': '',
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# 'WARNING_DATE': ''
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# }
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# }
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# query_data_list_item_nos_data = {
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# "funcModule": "数据项",
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# "funcOperation": "查询",
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# "data": {
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# "dateStart": "20200101",
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# "dateEnd": "20241231",
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# "dataItemNoList": ["Brentzdj", "Brentzgj"] # 数据项编码,代表 brent最低价和最高价
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# }
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# }
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# push_data_value_list_data = {
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# "funcModule": "数据表信息列表",
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# "funcOperation": "新增",
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# "data": [
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# {"dataItemNo": "91230600716676129",
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# "dataDate": "20230113",
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# "dataStatus": "add",
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# "dataValue": 100.11
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# },
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# {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
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# "dataDate": "20230113",
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# "dataStatus": "add",
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# "dataValue": 100.55
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# },
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# {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
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# "dataDate": "20230113",
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# "dataStatus": "add",
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# "dataValue": 100.55
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# }
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# ]
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# }
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# push_waring_data_value_list_data = {
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# "data": {
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# "crudeOilWarningDtoList": [
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# {
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# "lastUpdateDate": "20240501",
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# "updateSuspensionCycle": 1,
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# "dataSource": "8",
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# "frequency": "1",
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# "indicatorName": "美元指数",
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# "indicatorId": "myzs001",
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# "warningDate": "2024-05-13"
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# }
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# ],
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# "dataSource": "8"
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# },
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# "funcModule": "商品数据同步",
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# "funcOperation": "同步"
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# }
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# get_waring_data_value_list_data = {
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# "data": "8", "funcModule": "商品数据同步", "funcOperation": "同步"}
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# # 八大维度数据项编码
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# bdwd_items = {
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# 'ciri': '原油大数据预测|FORECAST|PRICE|T',
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# 'benzhou': '原油大数据预测|FORECAST|PRICE|W',
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# 'cizhou': '原油大数据预测|FORECAST|PRICE|W_1',
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# 'gezhou': '原油大数据预测|FORECAST|PRICE|W_2',
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# 'ciyue': '原油大数据预测|FORECAST|PRICE|M_1',
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# 'cieryue': '原油大数据预测|FORECAST|PRICE|M_2',
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# 'cisanyue': '原油大数据预测|FORECAST|PRICE|M_3',
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# 'cisiyue': '原油大数据预测|FORECAST|PRICE|M_4',
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# }
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# # 生产环境数据库
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# host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
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# port = 3306
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# dbusername = 'jingbo'
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# password = 'shihua@123'
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# dbname = 'jingbo'
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# table_name = 'v_tbl_crude_oil_warning'
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# # 变量定义--测试环境
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server_host = '192.168.100.53' # 内网
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# server_host = '183.242.74.28' # 外网
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login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
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# 上传报告
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upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
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# 停更预警
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upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
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# 查询数据项编码
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query_data_list_item_nos_url = f"http://{server_host}:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
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# 变量定义--线上环境
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server_host = '10.200.32.39'
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login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
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upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
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upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
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query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
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# 上传数据项值
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push_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/dw/dataValue/pushDataValueList"
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push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
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# 上传停更数据到市场信息平台
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push_waring_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/crudeSaveOrupdate"
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push_waring_data_value_list_url = f"http://{server_host}/jingbo-api/api/basicBuiness/crudeOilWarning/crudeSaveOrupdate"
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# 获取预警数据中取消订阅指标ID
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get_waring_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/dataList"
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get_waring_data_value_list_url = f"http://{server_host}/jingbo-api/api/basicBuiness/crudeOilWarning/dataList"
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login_data = {
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"data": {
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"account": "api_test",
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# "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
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"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
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"account": "api_dev",
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"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
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"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
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"terminal": "API"
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},
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@ -257,25 +118,24 @@ login_data = {
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"funcOperation": "获取token"
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}
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upload_data = {
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"groupNo": '', # 用户组id
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"funcModule": '研究报告信息',
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"funcOperation": '上传原油价格预测报告',
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"data": {
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"ownerAccount": 'arui', # 报告所属用户账号
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"groupNo": '', # 用户组id
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"ownerAccount": '27663', # 报告所属用户账号 27663 - 刘小朋
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"reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
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"fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', # 文件名称
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"fileName": '', # 文件名称
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"fileBase64": '', # 文件内容base64
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"categoryNo": 'yyjgycbg', # 研究报告分类编码
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"smartBusinessClassCode": 'YCJGYCBG', # 分析报告分类编码
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"reportEmployeeCode": "E40116", # 报告人
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"reportDeptCode": "D0044", # 报告部门
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"reportEmployeeCode": "E40482", # 报告人 E40482 - 管理员 0000027663 - 刘小朋
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"reportDeptCode": "002000621000", # 报告部门 - 002000621000 SH期货研究部
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"productGroupCode": "RAW_MATERIAL" # 商品分类
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}
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}
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# 已弃用
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warning_data = {
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"groupNo": '', # 用户组id
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"funcModule": '原油特征停更预警',
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@ -297,6 +157,7 @@ query_data_list_item_nos_data = {
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}
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}
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push_data_value_list_data = {
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"funcModule": "数据表信息列表",
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"funcOperation": "新增",
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@ -319,6 +180,7 @@ push_data_value_list_data = {
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]
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}
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push_waring_data_value_list_data = {
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"data": {
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"crudeOilWarningDtoList": [
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@ -342,27 +204,165 @@ push_waring_data_value_list_data = {
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get_waring_data_value_list_data = {
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"data": "8", "funcModule": "商品数据同步", "funcOperation": "同步"}
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# 八大维度数据项编码
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bdwd_items = {
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'ciri': 'yyycbdwdcr',
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'benzhou': 'yyycbdwdbz',
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'cizhou': 'yyycbdwdcz',
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'gezhou': 'yyycbdwdgz',
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'ciyue': 'yyycbdwdcy',
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'cieryue': 'yyycbdwdcey',
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'cisanyue': 'yyycbdwdcsy',
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'cisiyue': 'yyycbdwdcsiy',
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'ciri': '原油大数据预测|FORECAST|PRICE|T',
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'benzhou': '原油大数据预测|FORECAST|PRICE|W',
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'cizhou': '原油大数据预测|FORECAST|PRICE|W_1',
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'gezhou': '原油大数据预测|FORECAST|PRICE|W_2',
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'ciyue': '原油大数据预测|FORECAST|PRICE|M_1',
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'cieryue': '原油大数据预测|FORECAST|PRICE|M_2',
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'cisanyue': '原油大数据预测|FORECAST|PRICE|M_3',
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'cisiyue': '原油大数据预测|FORECAST|PRICE|M_4',
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}
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# 北京环境数据库
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host = '192.168.101.27'
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# 生产环境数据库
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host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
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port = 3306
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dbusername = 'root'
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password = '123456'
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dbname = 'jingbo_test'
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dbusername = 'jingbo'
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password = 'shihua@123'
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dbname = 'jingbo'
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table_name = 'v_tbl_crude_oil_warning'
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# # # 变量定义--测试环境
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# server_host = '192.168.100.53' # 内网
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# # server_host = '183.242.74.28' # 外网
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# login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
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# # 上传报告
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# upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
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# # 停更预警
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# upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
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# # 查询数据项编码
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# query_data_list_item_nos_url = f"http://{server_host}:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
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# # 上传数据项值
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# push_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/dw/dataValue/pushDataValueList"
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# # 上传停更数据到市场信息平台
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# push_waring_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/crudeSaveOrupdate"
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# # 获取预警数据中取消订阅指标ID
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# get_waring_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/dataList"
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# login_data = {
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# "data": {
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# "account": "api_test",
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# # "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
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# "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
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# "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
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# "terminal": "API"
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# },
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# "funcModule": "API",
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# "funcOperation": "获取token"
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# }
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# upload_data = {
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# "groupNo": '', # 用户组id
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# "funcModule": '研究报告信息',
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# "funcOperation": '上传原油价格预测报告',
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# "data": {
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# "ownerAccount": 'arui', # 报告所属用户账号
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# "reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
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# "fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', # 文件名称
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# "fileBase64": '', # 文件内容base64
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# "categoryNo": 'yyjgycbg', # 研究报告分类编码
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# "smartBusinessClassCode": 'YCJGYCBG', # 分析报告分类编码
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# "reportEmployeeCode": "E40116", # 报告人
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# "reportDeptCode": "D0044", # 报告部门
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# "productGroupCode": "RAW_MATERIAL" # 商品分类
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# }
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# }
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# # 已弃用
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# warning_data = {
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# "groupNo": '', # 用户组id
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# "funcModule": '原油特征停更预警',
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# "funcOperation": '原油特征停更预警',
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# "data": {
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# 'WARNING_TYPE_NAME': '特征数据停更预警',
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# 'WARNING_CONTENT': '',
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# 'WARNING_DATE': ''
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# }
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# }
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# query_data_list_item_nos_data = {
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# "funcModule": "数据项",
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# "funcOperation": "查询",
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# "data": {
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# "dateStart": "20200101",
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# "dateEnd": "20241231",
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# "dataItemNoList": ["Brentzdj", "Brentzgj"] # 数据项编码,代表 brent最低价和最高价
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# }
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# }
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# push_data_value_list_data = {
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# "funcModule": "数据表信息列表",
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# "funcOperation": "新增",
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# "data": [
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# {"dataItemNo": "91230600716676129",
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# "dataDate": "20230113",
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# "dataStatus": "add",
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# "dataValue": 100.11
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# },
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# {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
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# "dataDate": "20230113",
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# "dataStatus": "add",
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# "dataValue": 100.55
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# },
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# {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
# "dataDate": "20230113",
|
||||
# "dataStatus": "add",
|
||||
# "dataValue": 100.55
|
||||
# }
|
||||
# ]
|
||||
# }
|
||||
|
||||
# push_waring_data_value_list_data = {
|
||||
# "data": {
|
||||
# "crudeOilWarningDtoList": [
|
||||
# {
|
||||
# "lastUpdateDate": "20240501",
|
||||
# "updateSuspensionCycle": 1,
|
||||
# "dataSource": "8",
|
||||
# "frequency": "1",
|
||||
# "indicatorName": "美元指数",
|
||||
# "indicatorId": "myzs001",
|
||||
# "warningDate": "2024-05-13"
|
||||
# }
|
||||
# ],
|
||||
# "dataSource": "8"
|
||||
# },
|
||||
# "funcModule": "商品数据同步",
|
||||
# "funcOperation": "同步"
|
||||
# }
|
||||
|
||||
|
||||
# get_waring_data_value_list_data = {
|
||||
# "data": "8", "funcModule": "商品数据同步", "funcOperation": "同步"}
|
||||
|
||||
# # 八大维度数据项编码
|
||||
# bdwd_items = {
|
||||
# 'ciri': 'yyycbdwdcr',
|
||||
# 'benzhou': 'yyycbdwdbz',
|
||||
# 'cizhou': 'yyycbdwdcz',
|
||||
# 'gezhou': 'yyycbdwdgz',
|
||||
# 'ciyue': 'yyycbdwdcy',
|
||||
# 'cieryue': 'yyycbdwdcey',
|
||||
# 'cisanyue': 'yyycbdwdcsy',
|
||||
# 'cisiyue': 'yyycbdwdcsiy',
|
||||
# }
|
||||
|
||||
|
||||
# # 北京环境数据库
|
||||
# host = '192.168.101.27'
|
||||
# port = 3306
|
||||
# dbusername = 'root'
|
||||
# password = '123456'
|
||||
# dbname = 'jingbo_test'
|
||||
# table_name = 'v_tbl_crude_oil_warning'
|
||||
|
||||
DEFAULT_CONFIG = {
|
||||
'feature_factor_frequency': 'D',
|
||||
'strategy_id': 1,
|
||||
|
||||
@ -172,131 +172,19 @@ data = {
|
||||
ClassifyId = 1214
|
||||
|
||||
|
||||
# # 变量定义--线上环境
|
||||
# server_host = '10.200.32.39'
|
||||
# login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
|
||||
# upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
|
||||
# upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
|
||||
# query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
# # 上传数据项值
|
||||
# push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
|
||||
|
||||
# login_data = {
|
||||
# "data": {
|
||||
# "account": "api_dev",
|
||||
# "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
|
||||
# "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
# "terminal": "API"
|
||||
# },
|
||||
# "funcModule": "API",
|
||||
# "funcOperation": "获取token"
|
||||
# }
|
||||
|
||||
|
||||
# upload_data = {
|
||||
# "funcModule": '研究报告信息',
|
||||
# "funcOperation": '上传原油价格预测报告',
|
||||
# "data": {
|
||||
# "groupNo": '', # 用户组id
|
||||
# "ownerAccount": '27663', # 报告所属用户账号 27663 - 刘小朋
|
||||
# "reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
|
||||
# "fileName": '', # 文件名称
|
||||
# "fileBase64": '', # 文件内容base64
|
||||
# "categoryNo": 'yyjgycbg', # 研究报告分类编码
|
||||
# "smartBusinessClassCode": 'YCJGYCBG', # 分析报告分类编码
|
||||
# "reportEmployeeCode": "E40482", # 报告人 E40482 - 管理员 0000027663 - 刘小朋
|
||||
# "reportDeptCode": "002000621000", # 报告部门 - 002000621000 SH期货研究部
|
||||
# "productGroupCode": "RAW_MATERIAL" # 商品分类
|
||||
# }
|
||||
# }
|
||||
|
||||
# warning_data = {
|
||||
# "groupNo": '', # 用户组id
|
||||
# "funcModule": '原油特征停更预警',
|
||||
# "funcOperation": '原油特征停更预警',
|
||||
# "data": {
|
||||
# 'WARNING_TYPE_NAME': '特征数据停更预警',
|
||||
# 'WARNING_CONTENT': '',
|
||||
# 'WARNING_DATE': ''
|
||||
# }
|
||||
# }
|
||||
|
||||
# query_data_list_item_nos_data = {
|
||||
# "funcModule": "数据项",
|
||||
# "funcOperation": "查询",
|
||||
# "data": {
|
||||
# "dateStart": "20200101",
|
||||
# "dateEnd": "20241231",
|
||||
# "dataItemNoList": ["Brentzdj", "Brentzgj"] # 数据项编码,代表 brent最低价和最高价
|
||||
# }
|
||||
# }
|
||||
|
||||
|
||||
# push_data_value_list_data = {
|
||||
# "funcModule": "数据表信息列表",
|
||||
# "funcOperation": "新增",
|
||||
# "data": [
|
||||
# {"dataItemNo": "91230600716676129",
|
||||
# "dataDate": "20230113",
|
||||
# "dataStatus": "add",
|
||||
# "dataValue": 100.11
|
||||
# },
|
||||
# {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
# "dataDate": "20230113",
|
||||
# "dataStatus": "add",
|
||||
# "dataValue": 100.55
|
||||
# },
|
||||
# {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
# "dataDate": "20230113",
|
||||
# "dataStatus": "add",
|
||||
# "dataValue": 100.55
|
||||
# }
|
||||
# ]
|
||||
# }
|
||||
# # 八大维度数据项编码
|
||||
# bdwd_items = {
|
||||
# 'ciri': '原油大数据预测|FORECAST|PRICE|T',
|
||||
# 'benzhou': '原油大数据预测|FORECAST|PRICE|W',
|
||||
# 'cizhou': '原油大数据预测|FORECAST|PRICE|W_1',
|
||||
# 'gezhou': '原油大数据预测|FORECAST|PRICE|W_2',
|
||||
# 'ciyue': '原油大数据预测|FORECAST|PRICE|M_1',
|
||||
# 'cieryue': '原油大数据预测|FORECAST|PRICE|M_2',
|
||||
# 'cisanyue': '原油大数据预测|FORECAST|PRICE|M_3',
|
||||
# 'cisiyue': '原油大数据预测|FORECAST|PRICE|M_4',
|
||||
# }
|
||||
|
||||
# # 报告中八大维度数据项重命名
|
||||
# columnsrename = {
|
||||
# '原油大数据预测|FORECAST|PRICE|T': '次日', '原油大数据预测|FORECAST|PRICE|W': '本周',
|
||||
# '原油大数据预测|FORECAST|PRICE|W_1': '次周', '原油大数据预测|FORECAST|PRICE|W_2': '隔周',
|
||||
# '原油大数据预测|FORECAST|PRICE|M_1': '次月', '原油大数据预测|FORECAST|PRICE|M_2': '次二月',
|
||||
# '原油大数据预测|FORECAST|PRICE|M_3': '次三月', '原油大数据预测|FORECAST|PRICE|M_4': '次四月'
|
||||
# }
|
||||
|
||||
# # 生产环境数据库
|
||||
# host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
|
||||
# port = 3306
|
||||
# dbusername = 'jingbo'
|
||||
# password = 'shihua@123'
|
||||
# dbname = 'jingbo'
|
||||
# table_name = 'v_tbl_crude_oil_warning'
|
||||
|
||||
|
||||
# 变量定义--测试环境
|
||||
server_host = '192.168.100.53:8080' # 内网
|
||||
# server_host = '183.242.74.28' # 外网
|
||||
login_pushreport_url = f"http://{server_host}/jingbo-dev/api/server/login"
|
||||
upload_url = f"http://{server_host}/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
|
||||
upload_warning_url = f"http://{server_host}/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
|
||||
query_data_list_item_nos_url = f"http://{server_host}/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
# 变量定义--线上环境
|
||||
server_host = '10.200.32.39'
|
||||
login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
|
||||
upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
|
||||
upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
|
||||
query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
# 上传数据项值
|
||||
push_data_value_list_url = f"http://{server_host}/jingbo-dev/api/dw/dataValue/pushDataValueList"
|
||||
push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
|
||||
|
||||
login_data = {
|
||||
"data": {
|
||||
"account": "api_test",
|
||||
# "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
|
||||
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
|
||||
"account": "api_dev",
|
||||
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
|
||||
"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
"terminal": "API"
|
||||
},
|
||||
@ -304,24 +192,24 @@ login_data = {
|
||||
"funcOperation": "获取token"
|
||||
}
|
||||
|
||||
|
||||
upload_data = {
|
||||
"groupNo": '', # 用户组id
|
||||
"funcModule": '研究报告信息',
|
||||
"funcOperation": '上传原油价格预测报告',
|
||||
"data": {
|
||||
"ownerAccount": 'arui', # 报告所属用户账号
|
||||
"groupNo": '', # 用户组id
|
||||
"ownerAccount": '27663', # 报告所属用户账号 27663 - 刘小朋
|
||||
"reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
|
||||
"fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', # 文件名称
|
||||
"fileName": '', # 文件名称
|
||||
"fileBase64": '', # 文件内容base64
|
||||
"categoryNo": 'yyjgycbg', # 研究报告分类编码
|
||||
"smartBusinessClassCode": 'YCJGYCBG', # 分析报告分类编码
|
||||
"reportEmployeeCode": "E40116", # 报告人
|
||||
"reportDeptCode": "D0044", # 报告部门
|
||||
"reportEmployeeCode": "E40482", # 报告人 E40482 - 管理员 0000027663 - 刘小朋
|
||||
"reportDeptCode": "002000621000", # 报告部门 - 002000621000 SH期货研究部
|
||||
"productGroupCode": "RAW_MATERIAL" # 商品分类
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
warning_data = {
|
||||
"groupNo": '', # 用户组id
|
||||
"funcModule": '原油特征停更预警',
|
||||
@ -343,6 +231,7 @@ query_data_list_item_nos_data = {
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
push_data_value_list_data = {
|
||||
"funcModule": "数据表信息列表",
|
||||
"funcOperation": "新增",
|
||||
@ -366,27 +255,138 @@ push_data_value_list_data = {
|
||||
}
|
||||
# 八大维度数据项编码
|
||||
bdwd_items = {
|
||||
'ciri': 'yyycbdwdcr',
|
||||
'benzhou': 'yyycbdwdbz',
|
||||
'cizhou': 'yyycbdwdcz',
|
||||
'gezhou': 'yyycbdwdgz',
|
||||
'ciyue': 'yyycbdwdcy',
|
||||
'cieryue': 'yyycbdwdcey',
|
||||
'cisanyue': 'yyycbdwdcsy',
|
||||
'cisiyue': 'yyycbdwdcsiy',
|
||||
'ciri': '原油大数据预测|FORECAST|PRICE|T',
|
||||
'benzhou': '原油大数据预测|FORECAST|PRICE|W',
|
||||
'cizhou': '原油大数据预测|FORECAST|PRICE|W_1',
|
||||
'gezhou': '原油大数据预测|FORECAST|PRICE|W_2',
|
||||
'ciyue': '原油大数据预测|FORECAST|PRICE|M_1',
|
||||
'cieryue': '原油大数据预测|FORECAST|PRICE|M_2',
|
||||
'cisanyue': '原油大数据预测|FORECAST|PRICE|M_3',
|
||||
'cisiyue': '原油大数据预测|FORECAST|PRICE|M_4',
|
||||
}
|
||||
|
||||
# 报告中八大维度数据项重命名
|
||||
columnsrename = {'yyycbdwdbz': '本周', 'yyycbdwdcey': '次二月', 'yyycbdwdcr': '次日', 'yyycbdwdcsiy': '次四月',
|
||||
'yyycbdwdcsy': '次三月', 'yyycbdwdcy': '次月', 'yyycbdwdcz': '次周', 'yyycbdwdgz': '隔周', }
|
||||
# 北京环境数据库
|
||||
host = '192.168.101.27'
|
||||
columnsrename = {
|
||||
'原油大数据预测|FORECAST|PRICE|T': '次日', '原油大数据预测|FORECAST|PRICE|W': '本周',
|
||||
'原油大数据预测|FORECAST|PRICE|W_1': '次周', '原油大数据预测|FORECAST|PRICE|W_2': '隔周',
|
||||
'原油大数据预测|FORECAST|PRICE|M_1': '次月', '原油大数据预测|FORECAST|PRICE|M_2': '次二月',
|
||||
'原油大数据预测|FORECAST|PRICE|M_3': '次三月', '原油大数据预测|FORECAST|PRICE|M_4': '次四月'
|
||||
}
|
||||
|
||||
# 生产环境数据库
|
||||
host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
|
||||
port = 3306
|
||||
dbusername = 'root'
|
||||
password = '123456'
|
||||
dbname = 'jingbo_test'
|
||||
dbusername = 'jingbo'
|
||||
password = 'shihua@123'
|
||||
dbname = 'jingbo'
|
||||
table_name = 'v_tbl_crude_oil_warning'
|
||||
|
||||
|
||||
# # 变量定义--测试环境
|
||||
# server_host = '192.168.100.53:8080' # 内网
|
||||
# # server_host = '183.242.74.28' # 外网
|
||||
# login_pushreport_url = f"http://{server_host}/jingbo-dev/api/server/login"
|
||||
# upload_url = f"http://{server_host}/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
|
||||
# upload_warning_url = f"http://{server_host}/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
|
||||
# query_data_list_item_nos_url = f"http://{server_host}/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
# # 上传数据项值
|
||||
# push_data_value_list_url = f"http://{server_host}/jingbo-dev/api/dw/dataValue/pushDataValueList"
|
||||
|
||||
# login_data = {
|
||||
# "data": {
|
||||
# "account": "api_test",
|
||||
# # "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
|
||||
# "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
|
||||
# "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
# "terminal": "API"
|
||||
# },
|
||||
# "funcModule": "API",
|
||||
# "funcOperation": "获取token"
|
||||
# }
|
||||
|
||||
# upload_data = {
|
||||
# "groupNo": '', # 用户组id
|
||||
# "funcModule": '研究报告信息',
|
||||
# "funcOperation": '上传原油价格预测报告',
|
||||
# "data": {
|
||||
# "ownerAccount": 'arui', # 报告所属用户账号
|
||||
# "reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
|
||||
# "fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', # 文件名称
|
||||
# "fileBase64": '', # 文件内容base64
|
||||
# "categoryNo": 'yyjgycbg', # 研究报告分类编码
|
||||
# "smartBusinessClassCode": 'YCJGYCBG', # 分析报告分类编码
|
||||
# "reportEmployeeCode": "E40116", # 报告人
|
||||
# "reportDeptCode": "D0044", # 报告部门
|
||||
# "productGroupCode": "RAW_MATERIAL" # 商品分类
|
||||
# }
|
||||
# }
|
||||
|
||||
|
||||
# warning_data = {
|
||||
# "groupNo": '', # 用户组id
|
||||
# "funcModule": '原油特征停更预警',
|
||||
# "funcOperation": '原油特征停更预警',
|
||||
# "data": {
|
||||
# 'WARNING_TYPE_NAME': '特征数据停更预警',
|
||||
# 'WARNING_CONTENT': '',
|
||||
# 'WARNING_DATE': ''
|
||||
# }
|
||||
# }
|
||||
|
||||
# query_data_list_item_nos_data = {
|
||||
# "funcModule": "数据项",
|
||||
# "funcOperation": "查询",
|
||||
# "data": {
|
||||
# "dateStart": "20200101",
|
||||
# "dateEnd": "20241231",
|
||||
# "dataItemNoList": ["Brentzdj", "Brentzgj"] # 数据项编码,代表 brent最低价和最高价
|
||||
# }
|
||||
# }
|
||||
|
||||
# push_data_value_list_data = {
|
||||
# "funcModule": "数据表信息列表",
|
||||
# "funcOperation": "新增",
|
||||
# "data": [
|
||||
# {"dataItemNo": "91230600716676129",
|
||||
# "dataDate": "20230113",
|
||||
# "dataStatus": "add",
|
||||
# "dataValue": 100.11
|
||||
# },
|
||||
# {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
# "dataDate": "20230113",
|
||||
# "dataStatus": "add",
|
||||
# "dataValue": 100.55
|
||||
# },
|
||||
# {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
# "dataDate": "20230113",
|
||||
# "dataStatus": "add",
|
||||
# "dataValue": 100.55
|
||||
# }
|
||||
# ]
|
||||
# }
|
||||
# # 八大维度数据项编码
|
||||
# bdwd_items = {
|
||||
# 'ciri': 'yyycbdwdcr',
|
||||
# 'benzhou': 'yyycbdwdbz',
|
||||
# 'cizhou': 'yyycbdwdcz',
|
||||
# 'gezhou': 'yyycbdwdgz',
|
||||
# 'ciyue': 'yyycbdwdcy',
|
||||
# 'cieryue': 'yyycbdwdcey',
|
||||
# 'cisanyue': 'yyycbdwdcsy',
|
||||
# 'cisiyue': 'yyycbdwdcsiy',
|
||||
# }
|
||||
|
||||
# # 报告中八大维度数据项重命名
|
||||
# columnsrename = {'yyycbdwdbz': '本周', 'yyycbdwdcey': '次二月', 'yyycbdwdcr': '次日', 'yyycbdwdcsiy': '次四月',
|
||||
# 'yyycbdwdcsy': '次三月', 'yyycbdwdcy': '次月', 'yyycbdwdcz': '次周', 'yyycbdwdgz': '隔周', }
|
||||
# # 北京环境数据库
|
||||
# host = '192.168.101.27'
|
||||
# port = 3306
|
||||
# dbusername = 'root'
|
||||
# password = '123456'
|
||||
# dbname = 'jingbo_test'
|
||||
# table_name = 'v_tbl_crude_oil_warning'
|
||||
|
||||
DEFAULT_CONFIG = {
|
||||
'feature_factor_frequency': 'D',
|
||||
'strategy_id': 1,
|
||||
|
||||
@ -119,125 +119,19 @@ data = {
|
||||
ClassifyId = 1214
|
||||
|
||||
|
||||
# # 变量定义--线上环境
|
||||
# server_host = '10.200.32.39'
|
||||
# login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
|
||||
# upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
|
||||
# upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
|
||||
# query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
# # 上传数据项值
|
||||
# push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
|
||||
|
||||
# login_data = {
|
||||
# "data": {
|
||||
# "account": "api_dev",
|
||||
# "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
|
||||
# "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
# "terminal": "API"
|
||||
# },
|
||||
# "funcModule": "API",
|
||||
# "funcOperation": "获取token"
|
||||
# }
|
||||
|
||||
|
||||
# upload_data = {
|
||||
# "funcModule": '研究报告信息',
|
||||
# "funcOperation": '上传原油价格预测报告',
|
||||
# "data": {
|
||||
# "groupNo": '', # 用户组id
|
||||
# "ownerAccount": '27663', # 报告所属用户账号 27663 - 刘小朋
|
||||
# "reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
|
||||
# "fileName": '', # 文件名称
|
||||
# "fileBase64": '', # 文件内容base64
|
||||
# "categoryNo": 'yyjgycbg', # 研究报告分类编码
|
||||
# "smartBusinessClassCode": 'YCJGYCBG', # 分析报告分类编码
|
||||
# "reportEmployeeCode": "E40482", # 报告人 E40482 - 管理员 0000027663 - 刘小朋
|
||||
# "reportDeptCode": "002000621000", # 报告部门 - 002000621000 SH期货研究部
|
||||
# "productGroupCode": "RAW_MATERIAL" # 商品分类
|
||||
# }
|
||||
# }
|
||||
|
||||
# warning_data = {
|
||||
# "groupNo": '', # 用户组id
|
||||
# "funcModule": '原油特征停更预警',
|
||||
# "funcOperation": '原油特征停更预警',
|
||||
# "data": {
|
||||
# 'WARNING_TYPE_NAME': '特征数据停更预警',
|
||||
# 'WARNING_CONTENT': '',
|
||||
# 'WARNING_DATE': ''
|
||||
# }
|
||||
# }
|
||||
|
||||
# query_data_list_item_nos_data = {
|
||||
# "funcModule": "数据项",
|
||||
# "funcOperation": "查询",
|
||||
# "data": {
|
||||
# "dateStart": "20200101",
|
||||
# "dateEnd": "20241231",
|
||||
# "dataItemNoList": ["Brentzdj", "Brentzgj"] # 数据项编码,代表 brent最低价和最高价
|
||||
# }
|
||||
# }
|
||||
|
||||
|
||||
# push_data_value_list_data = {
|
||||
# "funcModule": "数据表信息列表",
|
||||
# "funcOperation": "新增",
|
||||
# "data": [
|
||||
# {"dataItemNo": "91230600716676129",
|
||||
# "dataDate": "20230113",
|
||||
# "dataStatus": "add",
|
||||
# "dataValue": 100.11
|
||||
# },
|
||||
# {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
# "dataDate": "20230113",
|
||||
# "dataStatus": "add",
|
||||
# "dataValue": 100.55
|
||||
# },
|
||||
# {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
# "dataDate": "20230113",
|
||||
# "dataStatus": "add",
|
||||
# "dataValue": 100.55
|
||||
# }
|
||||
# ]
|
||||
# }
|
||||
# # 八大维度数据项编码
|
||||
# bdwd_items = {
|
||||
# 'ciri': '原油大数据预测|FORECAST|PRICE|T',
|
||||
# 'benzhou': '原油大数据预测|FORECAST|PRICE|W',
|
||||
# 'cizhou': '原油大数据预测|FORECAST|PRICE|W_1',
|
||||
# 'gezhou': '原油大数据预测|FORECAST|PRICE|W_2',
|
||||
# 'ciyue': '原油大数据预测|FORECAST|PRICE|M_1',
|
||||
# 'cieryue': '原油大数据预测|FORECAST|PRICE|M_2',
|
||||
# 'cisanyue': '原油大数据预测|FORECAST|PRICE|M_3',
|
||||
# 'cisiyue': '原油大数据预测|FORECAST|PRICE|M_4',
|
||||
# }
|
||||
|
||||
|
||||
# # 生产环境数据库
|
||||
# host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
|
||||
# port = 3306
|
||||
# dbusername = 'jingbo'
|
||||
# password = 'shihua@123'
|
||||
# dbname = 'jingbo'
|
||||
# table_name = 'v_tbl_crude_oil_warning'
|
||||
|
||||
|
||||
# # 变量定义--测试环境
|
||||
server_host = '192.168.100.53:8080' # 内网
|
||||
# server_host = '183.242.74.28' # 外网
|
||||
|
||||
login_pushreport_url = f"http://{server_host}/jingbo-dev/api/server/login"
|
||||
upload_url = f"http://{server_host}/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
|
||||
upload_warning_url = f"http://{server_host}/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
|
||||
query_data_list_item_nos_url = f"http://{server_host}/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
# 变量定义--线上环境
|
||||
server_host = '10.200.32.39'
|
||||
login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
|
||||
upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
|
||||
upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
|
||||
query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
# 上传数据项值
|
||||
push_data_value_list_url = f"http://{server_host}/jingbo-dev/api/dw/dataValue/pushDataValueList"
|
||||
push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
|
||||
|
||||
login_data = {
|
||||
"data": {
|
||||
"account": "api_test",
|
||||
# "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
|
||||
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
|
||||
"account": "api_dev",
|
||||
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
|
||||
"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
"terminal": "API"
|
||||
},
|
||||
@ -245,24 +139,26 @@ login_data = {
|
||||
"funcOperation": "获取token"
|
||||
}
|
||||
|
||||
|
||||
upload_data = {
|
||||
"funcModule": '研究报告信息',
|
||||
"funcOperation": '上传原油价格预测报告',
|
||||
"data": {
|
||||
"ownerAccount": 'arui', # 报告所属用户账号
|
||||
"groupNo": '', # 用户组id
|
||||
"ownerAccount": '27663', # 报告所属用户账号 27663 - 刘小朋
|
||||
"reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
|
||||
"fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', # 文件名称
|
||||
"fileName": '', # 文件名称
|
||||
"fileBase64": '', # 文件内容base64
|
||||
"categoryNo": 'yyjgycbg', # 研究报告分类编码
|
||||
"smartBusinessClassCode": 'YCJGYCBG', # 分析报告分类编码
|
||||
"reportEmployeeCode": "E40116", # 报告人
|
||||
"reportDeptCode": "D0044", # 报告部门
|
||||
"reportEmployeeCode": "E40482", # 报告人 E40482 - 管理员 0000027663 - 刘小朋
|
||||
"reportDeptCode": "002000621000", # 报告部门 - 002000621000 SH期货研究部
|
||||
"productGroupCode": "RAW_MATERIAL" # 商品分类
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
warning_data = {
|
||||
"groupNo": '', # 用户组id
|
||||
"funcModule": '原油特征停更预警',
|
||||
"funcOperation": '原油特征停更预警',
|
||||
"data": {
|
||||
@ -282,6 +178,7 @@ query_data_list_item_nos_data = {
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
push_data_value_list_data = {
|
||||
"funcModule": "数据表信息列表",
|
||||
"funcOperation": "新增",
|
||||
@ -305,26 +202,129 @@ push_data_value_list_data = {
|
||||
}
|
||||
# 八大维度数据项编码
|
||||
bdwd_items = {
|
||||
'ciri': 'yyycbdwdcr',
|
||||
'benzhou': 'yyycbdwdbz',
|
||||
'cizhou': 'yyycbdwdcz',
|
||||
'gezhou': 'yyycbdwdgz',
|
||||
'ciyue': 'yyycbdwdcy',
|
||||
'cieryue': 'yyycbdwdcey',
|
||||
'cisanyue': 'yyycbdwdcsy',
|
||||
'cisiyue': 'yyycbdwdcsiy',
|
||||
'ciri': '原油大数据预测|FORECAST|PRICE|T',
|
||||
'benzhou': '原油大数据预测|FORECAST|PRICE|W',
|
||||
'cizhou': '原油大数据预测|FORECAST|PRICE|W_1',
|
||||
'gezhou': '原油大数据预测|FORECAST|PRICE|W_2',
|
||||
'ciyue': '原油大数据预测|FORECAST|PRICE|M_1',
|
||||
'cieryue': '原油大数据预测|FORECAST|PRICE|M_2',
|
||||
'cisanyue': '原油大数据预测|FORECAST|PRICE|M_3',
|
||||
'cisiyue': '原油大数据预测|FORECAST|PRICE|M_4',
|
||||
}
|
||||
|
||||
|
||||
# 北京环境数据库
|
||||
host = '192.168.101.27'
|
||||
# 生产环境数据库
|
||||
host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
|
||||
port = 3306
|
||||
dbusername = 'root'
|
||||
password = '123456'
|
||||
dbname = 'jingbo_test'
|
||||
dbusername = 'jingbo'
|
||||
password = 'shihua@123'
|
||||
dbname = 'jingbo'
|
||||
table_name = 'v_tbl_crude_oil_warning'
|
||||
|
||||
|
||||
# # # 变量定义--测试环境
|
||||
# server_host = '192.168.100.53:8080' # 内网
|
||||
# # server_host = '183.242.74.28' # 外网
|
||||
|
||||
# login_pushreport_url = f"http://{server_host}/jingbo-dev/api/server/login"
|
||||
# upload_url = f"http://{server_host}/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
|
||||
# upload_warning_url = f"http://{server_host}/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
|
||||
# query_data_list_item_nos_url = f"http://{server_host}/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
# # 上传数据项值
|
||||
# push_data_value_list_url = f"http://{server_host}/jingbo-dev/api/dw/dataValue/pushDataValueList"
|
||||
|
||||
# login_data = {
|
||||
# "data": {
|
||||
# "account": "api_test",
|
||||
# # "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
|
||||
# "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
|
||||
# "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
# "terminal": "API"
|
||||
# },
|
||||
# "funcModule": "API",
|
||||
# "funcOperation": "获取token"
|
||||
# }
|
||||
|
||||
# upload_data = {
|
||||
# "funcModule": '研究报告信息',
|
||||
# "funcOperation": '上传原油价格预测报告',
|
||||
# "data": {
|
||||
# "ownerAccount": 'arui', # 报告所属用户账号
|
||||
# "reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
|
||||
# "fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', # 文件名称
|
||||
# "fileBase64": '', # 文件内容base64
|
||||
# "categoryNo": 'yyjgycbg', # 研究报告分类编码
|
||||
# "smartBusinessClassCode": 'YCJGYCBG', # 分析报告分类编码
|
||||
# "reportEmployeeCode": "E40116", # 报告人
|
||||
# "reportDeptCode": "D0044", # 报告部门
|
||||
# "productGroupCode": "RAW_MATERIAL" # 商品分类
|
||||
# }
|
||||
# }
|
||||
|
||||
|
||||
# warning_data = {
|
||||
# "funcModule": '原油特征停更预警',
|
||||
# "funcOperation": '原油特征停更预警',
|
||||
# "data": {
|
||||
# 'WARNING_TYPE_NAME': '特征数据停更预警',
|
||||
# 'WARNING_CONTENT': '',
|
||||
# 'WARNING_DATE': ''
|
||||
# }
|
||||
# }
|
||||
|
||||
# query_data_list_item_nos_data = {
|
||||
# "funcModule": "数据项",
|
||||
# "funcOperation": "查询",
|
||||
# "data": {
|
||||
# "dateStart": "20200101",
|
||||
# "dateEnd": "20241231",
|
||||
# "dataItemNoList": ["Brentzdj", "Brentzgj"] # 数据项编码,代表 brent最低价和最高价
|
||||
# }
|
||||
# }
|
||||
|
||||
# push_data_value_list_data = {
|
||||
# "funcModule": "数据表信息列表",
|
||||
# "funcOperation": "新增",
|
||||
# "data": [
|
||||
# {"dataItemNo": "91230600716676129",
|
||||
# "dataDate": "20230113",
|
||||
# "dataStatus": "add",
|
||||
# "dataValue": 100.11
|
||||
# },
|
||||
# {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
# "dataDate": "20230113",
|
||||
# "dataStatus": "add",
|
||||
# "dataValue": 100.55
|
||||
# },
|
||||
# {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
# "dataDate": "20230113",
|
||||
# "dataStatus": "add",
|
||||
# "dataValue": 100.55
|
||||
# }
|
||||
# ]
|
||||
# }
|
||||
# # 八大维度数据项编码
|
||||
# bdwd_items = {
|
||||
# 'ciri': 'yyycbdwdcr',
|
||||
# 'benzhou': 'yyycbdwdbz',
|
||||
# 'cizhou': 'yyycbdwdcz',
|
||||
# 'gezhou': 'yyycbdwdgz',
|
||||
# 'ciyue': 'yyycbdwdcy',
|
||||
# 'cieryue': 'yyycbdwdcey',
|
||||
# 'cisanyue': 'yyycbdwdcsy',
|
||||
# 'cisiyue': 'yyycbdwdcsiy',
|
||||
# }
|
||||
|
||||
|
||||
# # 北京环境数据库
|
||||
# host = '192.168.101.27'
|
||||
# port = 3306
|
||||
# dbusername = 'root'
|
||||
# password = '123456'
|
||||
# dbname = 'jingbo_test'
|
||||
# table_name = 'v_tbl_crude_oil_warning'
|
||||
|
||||
|
||||
DEFAULT_CONFIG = {
|
||||
'feature_factor_frequency': 'D',
|
||||
'strategy_id': 1,
|
||||
|
||||
@ -581,11 +581,11 @@ def predict_main():
|
||||
if __name__ == '__main__':
|
||||
# global end_time
|
||||
# # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
|
||||
for i_time in pd.date_range('2025-6-11', '2025-6-28', freq='B'):
|
||||
global_config['end_time'] = i_time.strftime('%Y-%m-%d')
|
||||
global_config['db_mysql'].connect()
|
||||
predict_main()
|
||||
# for i_time in pd.date_range('2025-6-19', '2025-6-28', freq='B'):
|
||||
# global_config['end_time'] = i_time.strftime('%Y-%m-%d')
|
||||
# global_config['db_mysql'].connect()
|
||||
# predict_main()
|
||||
|
||||
# predict_main()
|
||||
predict_main()
|
||||
# push_market_value()
|
||||
# sql_inset_predict(global_config=global_config)
|
||||
|
||||
@ -473,44 +473,44 @@ def predict_main():
|
||||
# except Exception as e:
|
||||
# logger.info(f'更新accuracy表的y值失败：{e}')
|
||||
|
||||
# 判断当前日期是不是周一
|
||||
is_weekday = datetime.datetime.now().weekday() == 0
|
||||
if is_weekday:
|
||||
logger.info('今天是周一，更新预测模型')
|
||||
# 计算最近60天预测残差最低的模型名称
|
||||
model_results = sqlitedb.select_data(
|
||||
'trueandpredict', order_by="ds DESC", limit="60")
|
||||
# 删除空值率为90%以上的列
|
||||
if len(model_results) > 10:
|
||||
model_results = model_results.dropna(
|
||||
thresh=len(model_results)*0.1, axis=1)
|
||||
# 删除空行
|
||||
model_results = model_results.dropna()
|
||||
modelnames = model_results.columns.to_list()[2:-1]
|
||||
for col in model_results[modelnames].select_dtypes(include=['object']).columns:
|
||||
model_results[col] = model_results[col].astype(np.float32)
|
||||
# 计算每个预测值与真实值之间的偏差率
|
||||
for model in modelnames:
|
||||
model_results[f'{model}_abs_error_rate'] = abs(
|
||||
model_results['y'] - model_results[model]) / model_results['y']
|
||||
# 获取每行对应的最小偏差率值
|
||||
min_abs_error_rate_values = model_results.apply(
|
||||
lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
|
||||
# 获取每行对应的最小偏差率值对应的列名
|
||||
min_abs_error_rate_column_name = model_results.apply(
|
||||
lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
|
||||
# 将列名索引转换为列名
|
||||
min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(
|
||||
lambda x: x.split('_')[0])
|
||||
# 取出现次数最多的模型名称
|
||||
most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
|
||||
logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
|
||||
# 保存结果到数据库
|
||||
if not sqlitedb.check_table_exists('most_model'):
|
||||
sqlitedb.create_table(
|
||||
'most_model', columns="ds datetime, most_common_model TEXT")
|
||||
sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
|
||||
'%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
||||
# # 判断当前日期是不是周一
|
||||
# is_weekday = datetime.datetime.now().weekday() == 0
|
||||
# if is_weekday:
|
||||
# logger.info('今天是周一，更新预测模型')
|
||||
# # 计算最近60天预测残差最低的模型名称
|
||||
# model_results = sqlitedb.select_data(
|
||||
# 'trueandpredict', order_by="ds DESC", limit="60")
|
||||
# # 删除空值率为90%以上的列
|
||||
# if len(model_results) > 10:
|
||||
# model_results = model_results.dropna(
|
||||
# thresh=len(model_results)*0.1, axis=1)
|
||||
# # 删除空行
|
||||
# model_results = model_results.dropna()
|
||||
# modelnames = model_results.columns.to_list()[2:-1]
|
||||
# for col in model_results[modelnames].select_dtypes(include=['object']).columns:
|
||||
# model_results[col] = model_results[col].astype(np.float32)
|
||||
# # 计算每个预测值与真实值之间的偏差率
|
||||
# for model in modelnames:
|
||||
# model_results[f'{model}_abs_error_rate'] = abs(
|
||||
# model_results['y'] - model_results[model]) / model_results['y']
|
||||
# # 获取每行对应的最小偏差率值
|
||||
# min_abs_error_rate_values = model_results.apply(
|
||||
# lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
|
||||
# # 获取每行对应的最小偏差率值对应的列名
|
||||
# min_abs_error_rate_column_name = model_results.apply(
|
||||
# lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
|
||||
# # 将列名索引转换为列名
|
||||
# min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(
|
||||
# lambda x: x.split('_')[0])
|
||||
# # 取出现次数最多的模型名称
|
||||
# most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
|
||||
# logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
|
||||
# # 保存结果到数据库
|
||||
# if not sqlitedb.check_table_exists('most_model'):
|
||||
# sqlitedb.create_table(
|
||||
# 'most_model', columns="ds datetime, most_common_model TEXT")
|
||||
# sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
|
||||
# '%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
||||
|
||||
if is_corr:
|
||||
df = corr_feature(df=df)
|
||||
@ -590,13 +590,10 @@ def predict_main():
|
||||
if __name__ == '__main__':
|
||||
# global end_time
|
||||
# 遍历2024-11-25 到 2024-12-3 之间的工作日日期
|
||||
# for i_time in pd.date_range('2025-3-13', '2025-3-31', freq='B'):
|
||||
# try:
|
||||
# global_config['end_time'] = i_time.strftime('%Y-%m-%d')
|
||||
# predict_main()
|
||||
# except Exception as e:
|
||||
# logger.info(f'预测失败：{e}')
|
||||
# continue
|
||||
for i_time in pd.date_range('2025-6-4', '2025-6-30', freq='B'):
|
||||
global_config['end_time'] = i_time.strftime('%Y-%m-%d')
|
||||
global_config['db_mysql'].connect()
|
||||
predict_main()
|
||||
|
||||
# predict_main()
|
||||
sql_inset_predict(global_config=global_config)
|
||||
# sql_inset_predict(global_config=global_config)
|
||||
|
||||
@ -383,44 +383,44 @@ def predict_main():
|
||||
# except Exception as e:
|
||||
# logger.info(f'更新accuracy表的y值失败：{e}')
|
||||
|
||||
# 判断当前日期是不是周一
|
||||
is_weekday = datetime.datetime.now().weekday() == 0
|
||||
if is_weekday:
|
||||
logger.info('今天是周一，更新预测模型')
|
||||
# 计算最近60天预测残差最低的模型名称
|
||||
model_results = sqlitedb.select_data(
|
||||
'trueandpredict', order_by="ds DESC", limit="60")
|
||||
# 删除空值率为90%以上的列
|
||||
if len(model_results) > 10:
|
||||
model_results = model_results.dropna(
|
||||
thresh=len(model_results)*0.1, axis=1)
|
||||
# 删除空行
|
||||
model_results = model_results.dropna()
|
||||
modelnames = model_results.columns.to_list()[2:-2]
|
||||
for col in model_results[modelnames].select_dtypes(include=['object']).columns:
|
||||
model_results[col] = model_results[col].astype(np.float32)
|
||||
# 计算每个预测值与真实值之间的偏差率
|
||||
for model in modelnames:
|
||||
model_results[f'{model}_abs_error_rate'] = abs(
|
||||
model_results['y'] - model_results[model]) / model_results['y']
|
||||
# 获取每行对应的最小偏差率值
|
||||
min_abs_error_rate_values = model_results.apply(
|
||||
lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
|
||||
# 获取每行对应的最小偏差率值对应的列名
|
||||
min_abs_error_rate_column_name = model_results.apply(
|
||||
lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
|
||||
# 将列名索引转换为列名
|
||||
min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(
|
||||
lambda x: x.split('_')[0])
|
||||
# 取出现次数最多的模型名称
|
||||
most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
|
||||
logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
|
||||
# 保存结果到数据库
|
||||
if not sqlitedb.check_table_exists('most_model'):
|
||||
sqlitedb.create_table(
|
||||
'most_model', columns="ds datetime, most_common_model TEXT")
|
||||
sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
|
||||
'%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
||||
# 判断当前日期是不是周一 预测目标周度许转换，暂注释
|
||||
# is_weekday = datetime.datetime.strptime(global_config['end_time'], "%Y-%m-%d").weekday() == 0
|
||||
# if is_weekday:
|
||||
# logger.info('今天是周一，更新预测模型')
|
||||
# # 计算最近60天预测残差最低的模型名称
|
||||
# model_results = sqlitedb.select_data(
|
||||
# 'trueandpredict', order_by="ds DESC", limit="60")
|
||||
# # 删除空值率为90%以上的列
|
||||
# if len(model_results) > 10:
|
||||
# model_results = model_results.dropna(
|
||||
# thresh=len(model_results)*0.1, axis=1)
|
||||
# # 删除空行
|
||||
# model_results = model_results.dropna()
|
||||
# modelnames = model_results.columns.to_list()[2:-2]
|
||||
# for col in model_results[modelnames].select_dtypes(include=['object']).columns:
|
||||
# model_results[col] = model_results[col].astype(np.float32)
|
||||
# # 计算每个预测值与真实值之间的偏差率
|
||||
# for model in modelnames:
|
||||
# model_results[f'{model}_abs_error_rate'] = abs(
|
||||
# model_results['y'] - model_results[model]) / model_results['y']
|
||||
# # 获取每行对应的最小偏差率值
|
||||
# min_abs_error_rate_values = model_results.apply(
|
||||
# lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
|
||||
# # 获取每行对应的最小偏差率值对应的列名
|
||||
# min_abs_error_rate_column_name = model_results.apply(
|
||||
# lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
|
||||
# # 将列名索引转换为列名
|
||||
# min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(
|
||||
# lambda x: x.split('_')[0])
|
||||
# # 取出现次数最多的模型名称
|
||||
# most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
|
||||
# logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
|
||||
# # 保存结果到数据库
|
||||
# if not sqlitedb.check_table_exists('most_model'):
|
||||
# sqlitedb.create_table(
|
||||
# 'most_model', columns="ds datetime, most_common_model TEXT")
|
||||
# sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
|
||||
# '%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
||||
|
||||
if is_corr:
|
||||
df = corr_feature(df=df)
|
||||
@ -492,13 +492,10 @@ def predict_main():
|
||||
if __name__ == '__main__':
|
||||
# global end_time
|
||||
# 遍历2024-11-25 到 2024-12-3 之间的工作日日期
|
||||
# for i_time in pd.date_range('2025-2-1', '2025-3-31', freq='B'):
|
||||
# try:
|
||||
# global_config['end_time'] = i_time.strftime('%Y-%m-%d')
|
||||
# predict_main()
|
||||
# except Exception as e:
|
||||
# logger.info(f'预测失败：{e}')
|
||||
# continue
|
||||
for i_time in pd.date_range('2025-6-23', '2025-6-30', freq='B'):
|
||||
global_config['end_time'] = i_time.strftime('%Y-%m-%d')
|
||||
global_config['db_mysql'].connect()
|
||||
predict_main()
|
||||
|
||||
# predict_main()
|
||||
sql_inset_predict(global_config=global_config)
|
||||
# sql_inset_predict(global_config=global_config)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user